robust three-dimensional expansion of human adult alveolar ...jul 10, 2020 · development of...

TITLE: Robust three-dimensional expansion of human adult alveolar stem cells and

SARS-CoV-2 infection

Authors

Jeonghwan Youk (1,2,12), Taewoo Kim (1,12), Kelly V. Evans (3,4,12), Young-Il Jeong (5,12),

Yongsuk Hur (6,12), Seon Pyo Hong (7,12), Je Hyoung Kim (5), Kijong Yi (1), Su Yeon Kim (1), Kwon

Joong Na (8), Thomas Bleazard (9), Ho Min Kim (1,10), Natasha Ivory (11), Krishnaa T. Mahbubani

(11), Kourosh Saeb-Parsy (11), Young Tae Kim (8,*), Gou Young Koh (7,*), Byeong-Sun Choi (5,*),

Young Seok Ju (1,2,*) and Joo-Hyeon Lee (3,4,*)

Affiliations

1. Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and

Technology, Daejeon 34141, Republic of Korea

2. GENOMEINSIGHT Inc., Daejeon 34051, Republic of Korea

3. Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of

Cambridge, Cambridge, CB2 A0W, United Kingdom

4. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge,

CB2 3EL, United Kingdom

5. Division of Viral Disease Research, Center for Infectious Diseases Research, Korea National

Institute of Health, Korea Centers for Disease Control and Prevention, Cheongju 28159, Republic of

Korea

6. BioMedical Research Center, Korea Advanced institute of Science and Technology, Daejeon

34141, Republic Korea

7. Center for Vascular Research, Institute for Basic Science, Daejeon 34126, Republic of Korea

8. Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul

03080, Republic of Korea

9. The National Institute for Biological Standards and Control, Blanche Lane, South Mimms, Potters

Bar, Hertfordshire EN6 3QG, United Kingdom

.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is

The copyright holder for this preprintthis version posted July 10, 2020. ; https://doi.org/10.1101/2020.07.10.194498doi: bioRxiv preprint

https://doi.org/10.1101/2020.07.10.194498

http://creativecommons.org/licenses/by-nc-nd/4.0/

10. Center for Biomolecular and Cellular Structure, Institute for Basic Science, Daejeon 34126,

Republic of Korea

11. Department of Surgery and Cambridge NIHR Biomedical Research Centre, Biomedical Campus,

University of Cambridge, Cambridge, CB2 2QQ, United Kingdom

12. These authors contributed equally

* Correspondence to

Young Seok Ju, [email protected], +82-42-350-4237, Associate Professor, KAIST, Daejeon 34141,

Republic of Korea

Joo-Hyeon Lee, [email protected], Group Leader, Wellcome-MRC Cambridge Stem Cell Institute,

University of Cambridge, Cambridge CB2 A0W, United Kingdom

Byeong-Sun Choi, [email protected], Division head, Division of Viral Disease Research, Center

for Infectious Disease Research, Korea Centers for Disease Control & Prevention, Cheongju,

Republic of Korea

Gou Young Koh, [email protected], Director, Center for Vascular Research, Institute of Basic

Science, Daejeon 34126, Republic of Korea

Young Tae Kim, [email protected], Professor, Department of Thoracic Surgery, Seoul National

University Hospital, Seoul 03080, Republic of Korea



mailto:[email protected]





https://doi.org/10.1101/2020.07.10.194498


Abstract

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), which is the cause of a present

global pandemic, infects human lung alveolar cells (hACs). Characterising the pathogenesis is crucial

for developing vaccines and therapeutics. However, the lack of models mirroring the cellular

physiology and pathology of hACs limits the study. Here, we develop a feeder-free, long-term three-

dimensional (3D) culture technique for human alveolar type 2 (hAT2) cells, and investigate infection

response to SARS-CoV-2. By imaging-based analysis and single-cell transcriptome profiling, we

reveal rapid viral replication and the increased expression of interferon-associated genes and pro-

inflammatory genes in infected hAT2 cells, indicating robust endogenous innate immune response.

Further tracing of viral mutations acquired during transmission identifies full infection of individual cells

effectively from a single viral entry. Our study provides deep insights into the pathogenesis of SARS-

CoV-2, and the application of long-term 3D hAT2 cultures as models for respiratory diseases.

Keywords: lung alveolar type 2 cells; SARS-CoV-2; COVID-19; co-culture; virus; organoid;

transcriptome; pathogenesis; single-cell RNA-seq; interferon



https://doi.org/10.1101/2020.07.10.194498


Main

Several members of the family Coronaviridae are transmitted from animals to humans and cause

severe respiratory diseases in affected individuals1. These include the severe acute respiratory

syndrome (SARS) and the Middle East respiratory syndrome (MERS) coronavirus. Currently,

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2

(SARS-CoV-2), is spreading globally2 and more than 11.8 million confirmed cases with ~542K deaths

have been reported worldwide as of 7th Jul 20203. The lung alveoli are the main target for these

emerging viruses4.

To develop strategies for efficient prevention, diagnosis, and treatment, the characteristics of new

viruses, including mechanisms of cell entry and transmission, kinetics in replication and transcription,

host reactions and genome evolution, should be accurately understood in target tissues. Although

basic molecular mechanisms in SARS-CoV-2 infection have been identified5-8, most findings have

been obtained from experiments using non-physiological cell lines9, model animals, such as

transgenic mice expressing human angiotensin-converting enzyme 2 (ACE2)10, ferrets11 and golden

hamsters12, or from observation in clinical cohorts13 and/or inference from in-silico computational

methods14-16. As a consequence, we do not fully understand how SARS-CoV-2 affects human lung

tissues in the physiological state.

Development of three-dimensional (3D) stem cell-derived organotypic culture models, conventionally

called organoids, has enabled various physiologic and pathological studies using human-derived

tissues in vitro17-19. Organoid models established from induced pluripotent stem cells (iPSCs), or adult

stem cells in the human kidney, intestine, and airway have been used to investigate SARS-CoV-2

pathogenesis20-23. Although human alveolar type 2 cells (hereafter referred to as hAT2s) are believed

to be the ultimate target cells for SARS-CoV-2, their infection model has not previously been

introduced.

Long-term 3D culturing of hAT2s without mesenchymal niche

We have established feeder-free, 3D hAT2 organoids (hereafter referred to as hAOs; definition of

organoid is available at ref. 24) with defined factors which support molecular and functional identity of



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hAT2 cells over multiple passages, showing substantial improvements from the previous application

of co-culture models25-27. Briefly, single-cell dissociated hAT2 cells were isolated by fluorescence-

activated cell sorting (FACS) for the hAT2 surface marker HTII-280 (CD31-CD45-EpCAM+HTII-

280+)25,28(Fig. 1a; Extended Data Fig. 1a). Isolated HTII-280+ cells showed higher expression of AT2

cell marker SFTPC, while HTII-280– cells revealed higher expressions of basal cell marker TP63 and

secretory cell marker SCGB1A1 (Extended Data Fig. 1b). We then plated HTII-280+ hAT2 cells into

Matrigel for 3D cultures with our expansion medium, supplemented with CHIR99021, RSPO1 (R-

spondin 1), FGF7, FGF10, EGF, NOG (Noggin), and SB431542, that are known to support the growth

of human embryonic lung tip cells29. HTII-280– cells were also cultured under conditions supporting

human bronchial (airway) organoids (hereafter referred to as hBOs) that have previously been

reported30. hAOs established from single hAT2 cells grew up to 4 weeks with heterogeneous

morphology including budding-like and cystic-like structures consisting of mature AT2 cells expressing

pro-SFTPC, HTII-280, and ABCA3, as well as exhibiting uptake of Lysotracker, a fluorescent dye that

stains acidic organelles such as lamellar bodies25 (Fig. 1b and 1c). In contrast, hBOs grew quickly by

day 14 with cystic-like structures consisting of a number of airway cell types, including KRT5+TP63+

basal cells and SCGB1A1+ secretory cells, as previously reported30 (Fig. 1d and 1e). WNT activation

was identified as an essential factor for hAO formation, because no colony formation was found in the

absence of WNT activator CHIR99021 in culture (Extended Data Fig. 1c). Importantly, our culture

system allows the long-term expansion (>10 months) of hAT2s, although colony forming efficiency

varied between tissue samples and reduced at later passages (Extended Data Fig. 1d). Over

passaging via single cells, hAT2s consistently formed organoids, exhibiting SFTPC expression

following 9 months of continuous cultures, although growth began to slow, as evident by reduced

organoid size and lower forming efficiencies (Extended Data Fig. 1d and 1e). Alveolar type 1 cells

(hAT1s) expressing HOPX and PDPN were also observed during early cultures, demonstrating

differentiation capacity of hAT2 cells in our hAOs (Extended Data Fig. 1f), although later passages

exhibited loss of these cells.

Robust infection of SARS-CoV-2 into hAOs

The expressions of ACE2 and TMPRSS2, which are necessary for SARS-CoV-2 infection, were

observed in the membrane and cytoplasm of hAO cells (Fig. 1f and 1g; Extended Data Fig. 1g). We



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next infected hAOs and hBOs with SARS-CoV-2 at a multiplicity of infection (MOI) of 1. The viral

particles were prepared from a patient (known as KCDC03) who was diagnosed with COVID-19 on

26th Jan, 2020, after traveling to Wuhan, China31. Vero cells were also infected as a positive control,

although this was not directly comparable to our 3D models due to different technical procedures.

Infectious virus particles increased to significant titers in hAOs (Fig. 1h-1k; Extended Data Fig. 2),

reaching maximum levels within the 1st day post infection (dpi), suggesting that full infection occurs

within 1 day from viral entry to hAOs. In hBOs, the increment of viral particles was observed as

consistent with another study32, but their titers were <100 times lower than hAOs (Fig. 1h and 1i;

Extended Data Fig. 2). In line with viral particles, the amount of the viral RNA in hAOs and in its

culture supernatant reached a plateau at 1 dpi (Fig. 1j and 1k). Although infected Vero cells exhibited

significant cytopathic effects at 1 dpi, typically cell rounding, detachment, degeneration and syncytium

formation9, SARS-CoV-2 infected hAOs and hBOs did not show prominent macroscopic pathologies

up until 10 dpi.

Structural changes of hAOs to SARS-CoV-2 infection

Immunostaining for double-stranded viral RNA (dsRNA) and nucleocapsid protein (NP) of SARS-

CoV-2 identified widespread viral infection in hAT2 cells co-expressing pro-SFTPC and ACE2 in

hAOs (Fig. 2a and 2b; Extended Data Fig. 3). To further determine subcellular events at a higher

resolution, transmission electron microscopic analysis was performed at 2 dpi (Fig. 2c-2l; Extended

Data Fig. 4). Most cells (~80%), containing secretory vesicles and surfactant proteins (lamellar

bodies25), showed discernible viral particles in the cytoplasm. A fraction of cells in hAOs showed

much higher viral burdens than other cells, with as many as 500 copies in the 100 nm section,

implying that >10,000 SARS-CoV-2 particles per cell. Aggregated viral proteins, which appeared as

electron-dense regions near nuclei33, were also detected (Fig. 2c). Accordingly, several key

pathogenic phenotypes were observed in the infected hAOs. Alveolar cells with enormous vacuoles

were frequently observed (Fig. 2c, 2f, 2h, 2i), similar to cytopathic signals in Zika virus34. Double-

membrane vesicles (DMVs), subcellular structures known as viral replication sites frequently seen in

the early phase of infection35,36, were observed in the vicinity of zippered endoplasmic reticulum in a

small fraction of hAO cells (Fig. 2j and 2k). Viral particles were dispersed in the cytosol (Fig. 2e) or



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enclosed in the small vesicular structures (Fig. 2f and 2i). Diverse forms of viral secretion were also

observed mainly through the apical surfaces of hAT2 cells (Fig. 2g and 2l). More ultrastructural

pathologies are available at Extended Data Fig. 4 and the EMPIAR data archive (see Data

availability).

Transcriptional changes of hAOs to SARS-CoV-2 infection

From strand-specific deep RNA-sequencing, we explored gene expression changes in the infected

hAOs. Indeed, a set of human genes were differentially expressed as infection progressed (i.e., 0, 1

and 3 dpi), although most genes showed good correlations (Extended Data Fig. 5a and

Supplementary Table 1). Cytokeratin genes (including KRT16, KRT6A, KRT6B, and KRT6C), genes

involved in keratinization (including SPRR1A), cytoskeleton (including S100A2) and cell-cell adhesion

genes (including DSG3), were significantly reduced to ~2-3% in hAOs at 3 dpi (Fig 3a and 3b). Many

more genes were upregulated in the infected hAOs specifically at 3 dpi. In particular, transcription of a

broad range of interferon-stimulated genes (ISGs), known to be typically activated by type I and III

interferons37, were remarkably increased (Fig. 3a and 3b). These genes include interferon induced

protein genes (such as IFI6, IFI27, IFI44, IFI44L), interferon induced transmembrane protein genes

(such as IFITM1), interferon induced transmembrane proteins with tetratricopeptide repeats genes

(IFIT1, IFIT2, IFIT3), 2’-5’-oligoadenylate synthetase genes (OAS1, OAS2), and miscellaneous genes

known to be involved in innate cellular immunity (MX1, MX2, RSAD2, ISG15). These genes were

expressed to >20 times higher levels in hAOs at 3 dpi than at 0 dpi. Many other known ISGs also

showed moderate inductions (2-20 times) at 3 dpi, including BTS2 (~15 times), OAS3 (~11 times),

HERC5 (~15 times), HERC6 (~12 times) and USP18 (~12 times). Antiviral functions are known for

these ISGs38 including (1) inhibition of virus entry (MX genes, IFITM genes), (2) inhibition of viral

replication and translation (IFIT genes, OAS genes, ISG15, HERC5, HERC6, USP18) and (3)

inhibition of viral egress (RSAD2 and BST2). Of note, given that immune cells are absent in our

culture system, the innate immune response was completely autologous to alveolar cells, mimicking

the initial phase of SARS-CoV-2 alveolar infection. In line with the notion, innate induction of some

type I and type III interferons was observed. Of the 20 interferon genes, an interferon beta gene

(IFNB1) and three interferon lambda genes (IFNL1, IFNL2, and IFNL3) showed significant

transcriptional induction, although their absolute changes were not substantial (Fig. 3c). The surface



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receptors of interferons were stably expressed in hAO cells without reference to viral infection (Fig.

3c). Downstream signalling genes of the receptors were also upregulated such as STAT1 (~3.5

times), STAT2 (~2.5 times) and their associated genes IRF1 (~2.4 times) and IRF9 (~6.4 times). Of

note, IRF1 is known to be specific to type I interferon responses39, while type I and type III ISGs are

generally overlapping 40.

In addition to ISGs, genes in the viral sensing pathway in cytosol showed increased expression in the

infected hAOs at 3 dpi, for example, DDX58 (official gene name of RIG-1, from 1.9 to 25.0 TPM),

IFIH1 (also known as MDA5, from 5.8 to 30.3 TPM), and TLR3 (Toll-like receptor 3, from 1.3 to 3.7

TPM), IRF7 (Interferon regulatory factor 7, from 4.4 to 29 TPM) and IL6 (0.6 to 2.5 at 1 dpi;

proinflammatory factor).

Notably, these transcriptional changes were much stronger in hAOs than in hBOs. In the similar

transcriptome profiling of the infected hBOs, the genes aforementioned were not significantly altered

(Supplementary Table 2 and Extended Data Fig. 5b). In addition, we identified few hBO specific

differentially expressed genes (Extended Data Fig. 5c). This finding implies cellular tropism of SARS-

CoV-2 viral infection.

Expression of SARS-CoV-2 genes in the infected organoids

We further analysed the viral RNA sequences obtained from the infected models. In agreement with

the plaque assay (Fig. 1h and 1i), relative transcription of SARS-CoV-2 genes plateaued by 1 dpi

(Fig. 3d), which is earlier than the host gene expression changes. Approximately 50% of the RNA

sequencing reads were mappable to the SARS-CoV-2 genome in hAOs from 1 dpi (Fig. 3d),

indicating prevailing viral gene expression in infected hAO cells as observed in Vero cells6. Of note,

the proportion of viral transcripts was much lower in the infected hBOs.

Transcripts from SARS-CoV-2 were not mapped uniformly to the viral genome sequence, but 3’

genomic regions, where canonical subgenomic RNAs are located, showed much higher read-depth in

all samples, consistent with the previous report6 (Fig. 3e). The vast majority of viral RNA sequences



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produced from the infected hAOs and hBOs was in the orientation of positive-sense RNA strands

(Fig. 3e; for example, 99.98% vs 0.02% for positive- and negative-sense RNAs, respectively, from

hAO at 1 dpi). This is in good agreement with the nature of SARS-CoV-2, which is an enveloped, non-

segmented, and positive-sense RNA virus.

By cross-comparison of viral RNA sequences produced from a total of 11 infected hAOs (n=5) and

hBCs (n=6), we identified 20 viral base substitutions (Supplementary Table 3). No mutation was at

100% variant allele fraction (VAF) and exclusive to an infected sample. Instead, sequence alterations

showed a broad range of quasispecies heterogeneity in each culture (VAF ranges from 0.1% to 73.1%;

Fig. 3f), and a large proportion of the mutations (n=16; 80%) were shared by two or more infected

models (by the cut-off threshold of 0.1%). Therefore, we speculate that most of these sequence

changes were originally present in the pool of viral particles before their inoculation. Given the fact that

these viral particles were prepared from one of the earliest COVID-19 patients, our finding suggests

that mutations can accumulate in the viral genomes in a small number of rounds of viral transmissions,

and appear with dramatic changes in quasispecies abundance. A substantially higher proportion of

specific mutations in a sample may suggest a bottleneck in viral entry or stochasticity in viral replication.

Transcriptome changes at single-cell resolution

To understand transcriptional changes of the infected hAOs at a single-cell resolution, we employed

two 10X Genomics single-cell RNA-seq experiments for uninfected and infected hAOs at 3 dpi (to a

throughput of 21.3 Gb and 28.5 Gb, respectively). We captured 3,435 and 3,475 single-cells,

respectively, with 15,703 UMIs and 3,414 detectable genes per cell on average. Using a total of 6,910

single-cells, we performed unsupervised clustering using UMIs of the human origin, and identified four

distinct clusters (Fig. 4a): Cluster 1, mostly with uninfected alveolar cells (3,133 cells); Cluster 2,

mostly with airway-like cells (361 cells; Supplementary Discussion); Cluster 3, alveolar cells with

moderate levels of viral RNA transcripts (2,143 cells); and Cluster 4, cells disintegrating and likely

close to cell death (n=1,273). The cells in the experiment of infected hAOs were mostly distributed in

Cluster 3 (2,142 cells; 61.6% of infected hAOs) and Cluster 4 (1,154 cells; 33.2% of infected hAOs)

(Fig. 4b). Viral transcripts were found in the vast majority of infected hAO cells (99.9%; 3,471 out of



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3,475 infected hAOs), validating our previous observation that most cells in the infected hAOs harbor

viral transcripts (Fig. 2). The number of viral transcripts, however, was not uniformly distributed in all

infected hAO cells, but enriched in Cluster 4 cells. Infected cells in Cluster 4 exhibited 13.7 times

more viral UMI counts than cells in Cluster 3, on average (Fig. 4c; 1,904 vs. 139 UMIs, respectively),

despite cells in Cluster 4 containing relatively lower total UMI counts than ones in Cluster 3 (5,040 vs.

17,843 UMIs, respectively). When normalised with UMI counts for human genes, cells in Cluster 4

showed a >30 times higher viral RNA burden than cells in Cluster 3 (Fig 4d). Interestingly, the

infected cells in Cluster 4 showed reduced expression of canonical hAT2 marker genes, including

SFTPB (Surfactant Protein B) and NKX2-1 (NK2 homeobox 1) (Fig. 4e; Extended Data Fig. 6a).

Compared with infected cells in Cluster 3, expression levels of ISGs, such as IFI44L and OAS3, were

also highly reduced. Instead, these cells showed transcriptional induction of apoptosis mediator,

GADD45B (growth arrest and DNA-damage-inducible, beta) and anti-apoptotic TNFAIP3 (tumor

necrosis factor, alpha-induced protein 3), suggesting a catastrophic cellular pathway operating in a

cell due to the extreme viral burdens.

Despite active protein expression (Fig. 1f and 1g; Extended Data Fig. 1g), we found 14 cells (0.4%)

showing ACE2 transcripts, 579 cells (16.7%) expressing TMPRSS2 transcripts, and 4 cells (0.1%) co-

expressing both in single-cell transcriptome sequencing (Fig. 4e). These proportions are low at face

value, but are consistent with a previous observation41. Although the previous report also suggested

that ACE2 RNA expression can be stimulated as an infection-mediated response, particularly in

human airway cells, such a trend was not observed in our dataset.

Effective number of viral entry for infection of an alveolar cell

Finally, we statistically inferred the number of viral particles effectively entering each alveolar cell for

infection. Although we incubated cells at an MOI of 1 on average, it is generally not known how many

viral particles are necessary for effective infection of an alveolar cell. In an extreme scenario, one viral

particle is sufficient. Alternatively, infection may be initiated with the entry of multiple viruses. We

tracked the effective viral number of cellular entry using a mutation (NC_045512.2: 23,707C>U) as a

viral barcode. From our sequencing, the mutation was estimated to be present at 4.3% VAF in the

initial viral pool for innoculation. If the first scenario dominantly applies, the infected alveolar cells will



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be dichotomized, i.e., 95.7% cells with solely wild-type virus and the rest of the cells with solely

mutant virus, exclusively. Alternatively, if multiple viruses are effectively entering a cell, a large

proportion of alveolar cells harbouring detectable mutant virus will have intermediate VAF for the

mutation. In our single-cell transcriptomes of infected hAOs, 212 cells were informative with the viral

mutation, containing at least two independent transcripts for the mutant locus. The majority of the

cells harbouring the mutant allele did not have the wild-type allele present (n=8 vs. 4 for cells showing

solely mutant allele vs mixture of the quasispecies, respectively; Fig. 4f). In a more sophisticated

statistical analysis, infection by single viral entry is estimated as >2 times more frequent than by

multiple viral entry (69% vs. 31%, respectively; Fig. 4g). Our calculation indicates that a single viral

particle is mainly responsible for SARS-CoV-2 infection in most alveolar cells, although multiple viral

entry is also possible. It may also reflect the viral interference in SARS-CoV-2 alveolar infection.

Discussion

In this study, we established conditions for optimised 3D long-term cultures of adult hAT2 cells, which

provided an essential tool for studying initial intrinsic responses of SARS-CoV-2 infection. Single

hAT2 cells were capable of self-constructing alveolus-like structures consisting of AT2 cell and

differentiated AT1 cells. Mature hAT2 cells were maintained >10 months over multiple passages

although self-renewal capacity and growth rate was reduced after 6 month in cultures. hAT1 cells

were also lost from later culture, likely due to the persistent exposure to high WNT conditions allowing

expansion of hAT2 cells over differentiation. Alteration of WNT activity in culture media (differentiation

media) may enable to induce further AT1 cell differentiation.

hAOs showed remarkable phenotypic changes in the first few days after SARS-CoV-2 innoculation.

Our single-cell transcriptome profiling identified two clusters of infected cells (Clusters 3 and 4),

suggesting a distinct switch between cell states occurring during infection. If in vivo SARS-CoV-2

infection transforms hAT2 cells into Cluster 4-like cells showing the loss of hAT2 identity, the integrity

and function of alveoli will be highly compromised. The route towards the quantum cellular change is

of significant interest to be answered in future studies. To this end, we need more single-cell RNA

sequencing from infected cells at earlier time points in combination with comprehensive high-

resolution imaging.



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The interferon response is the first line of host antiviral defense40. Contrary to a recent report42, we

observed substantial ISGs in the alveolar cells induced by endogenously produced type I and III

interferons. However, the induction of interferon responses was seen at 3 dpi in hAO models, 1-2

days later than the timing of viral amplification at 1 dpi. The timing of ISG induction may be earlier in

vivo in concert with exogenous interferons from immune cells. For more physiological understanding,

co-culturing SARS-CoV-2 infected hAO models with immune cells obtained from the same donor will

be helpful.

In summary, our study highlights the power of feeder-free hAOs to elucidate the intrinsic responses of

tissue damage including virus infection. Our data, including high-resolution electron microscopic

images and the list of gene expression changes following infection, will be a great resource for the

biomedical community to provide a deeper characterisation of SARS-CoV-2 infection specifically

within adult hAT2 cells. We believe that our hAO models will enable more accurate and sophisticated

analyses in the very near future, especially for studying the response of viral infection within

vulnerable groups such as aged or diseased lungs, providing the opportunity to elucidate individual

patient responses to viral infection. Furthermore, our models can be applied to other techniques, such

as co-culture experiments with immune cells and robust in vitro screening of antiviral agents

applicable to alveolar cells, in addition to being applicable for the study of the basic biology of alveolar

cells as well as chronic disorders of the lung.



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Methods

Human Tissues

For the establishment of human lung organoid models, human distal lung parenchymal tissues from

deidentified lungs not required for transplantation were obtained from adult donors with no

background lung pathologies from Papworth Hospital Research Tissue Bank (T02233), and

Addenbrookes Hospital (Cambridge University NHS foundations trust) under the collaboration of

Cambridge Biorepository for Translational Medicine (CBTM) project. Appropriate Human Tissue Act

(HTA) guidance was followed; For organoids used for viral infection and following analysis, human

lung tissue was obtained from patients undergoing lobectomy surgery at Seoul National University

Hospital (SNUH) with written informed consent from approval of the ethical committee (approval no.

C-1809-137-975). Organoids for infection studies were established from adjacent normal tissues in

lung cancer patients or an idiopathic pulmonary fibrosis (IPF) patient.

Virus particle preparation for infection

SARS-CoV-2 viral particles known as BetaCov/Korea/KCDC03/20206,31 were used for the infection

study. The patient (KCDC03) was diagnosed with COVID-19 on January 26, 2020, after traveling to

Wuhan, China. The viral particles were prepared from Vero cells infected with MOI 0.01 and grown

under DMEM (Sigma) with 2% FBS(Gibco), 1% P/S(Gibco) for 48 hours at 37°C 5% CO2. Media was

centrifuged with 2500rpm for 25min, and supernatant without cell debris was stocked at -80°C with 4 x

106 pfu / ml.

Human lung tissue dissociation and flow cytometry

Distal lung parenchymal tissue was processed as soon as possible in order to minimize cell yield loss

and maintain cell viability. Briefly, fresh tissue was washed in cold PBS and minced into small (1 mm)

pieces with a scalpel, followed by further dissociation using pre-warmed digestion buffer containing

2U/ mL Dispase II (Sigma, Corning), 1 mg/mL Dispase/Collagenase (Sigma) and 0.1 mg/mL DNase I

(Sigma) in PBS at 37 ºC for 1 hr with agitation. Tissue cell suspensions were filtered through a 100

mM cell strainer into a 50 mL falcon tube to remove cell debris, and washed with 10 mL of DMEM



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(Gibco, Thermofisher). Cells were centrifuged at 350 g for 10 min, supernatant carefully aspirated,

and cell pellet resuspended in 5 mL RBC lysis buffer for 5 min at room temperature (RT). The reaction

was quenched using 5 mL of DMEM, and the entire 10 mL of cell suspension was transferred to a 15

mL falcon tube, followed by 10 min centrifugation at 350 g. Supernatant was removed, and the cell

pellet was resuspended in 10% FBS in PBS (PF10 buffer) for counting. Cells were prepared for flow

cytometry with primary antibodies CD31-APC (Biolegend, 303116), CD45-APC (Biolegend, 368512),

EpCAM-FITC (Biolegend, 324204) and HTII-280-IgM (Terrace Biotech, TB-27AHT2-280) at 1:40 per

4 million cells for 30 min on ice. Following two washes with cold PF10 buffer and centrifugation at 350

g for 5 min, cells were stained with secondary PE goat anti-mouse IgM (eBioscience, 12-5790-81) for

HTII-280 at 1:100. Stained cells were washed with PF10 buffer, and counted using a hemocytometer

to assess dilution required for final volume. Cells were diluted at a concentration of 30 million cells/

mL and filtered through a 35 µM cell strainer into polypropylene FACS tubes. Cell sorting was

performed on an Aria III fusion (BD Biosciences) using a 100 µM nozzle, and data were analysed with

FlowJo software (Tree Star, Inc.).

In vitro organoid culture and passage

Freshly isolated HTII-280+ and HTII-280- cells derived from CD31–CD45–EpCAM+ cells of human distal

lungs were resuspended in Advanced DMEM/F12 (Thermofisher) base medium supplemented with

10 mM Hepes (Gibco), 1 U/mL Penicillin/Streptomycin (Gibco), 1 mM N-Acetylcysteine (Sigma), and

10 mM Nicotinamide (Sigma). Growth factor-reduced (GFR-) Matrigel (Corning) was added to the cell

suspension at a ratio of 1:1, and 100 µL of suspension was added to a 24-well transwell insert with a

0.4 µM pore (Corning) so that there were approximately 10 x 103 cells per insert. GFR-Matrigel was

allowed to solidify for 1 hr at 37 ºC, after which 500 µL of pre-warmed alveolar media (base media

supplemented with 1 x B27 (ThermoFisher), 10% R-SPONDIN-1 (Cambridge Stem Cell Institute

tissue culture core facility, manually produced), 50 ng/ml human EGF (Peprotech), 100 ng/ml human

FGF7/KGF (Peprotech),100 ng/ml human FGF10 (Peprotech), 100 ng/ml NOGGIN (Peprotech),10 µM

SB431542 (Tocris) and 3 µM CHIR99021 (Tocris)) was added to each lower chamber. For

assessment of the effect of Wnt activity on hAT2 culture ability, primary cultures were also established

without CHIR99021. Cultures were maintained under standard cell culture conditions (37 °C, 5%

CO2), with media changes every 2-3 days. Y-27632 (10 µM, Sigma) was added for the first 48 hr of



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culture to promote cell survival. To avoid the growth of fungal and bacterial infection, 250 ng/mL

Amphotericin B and 50 µg/mL gentamicin were added to culture medium for 5 days. For culture in 48-

well plates, 5 x 103 cells were resuspended in 100% GFR-Matrigel, and allowed to solidify in a 20 µL

droplet per well at 37 ºC for 20 min, followed by submersion in 250 µL of pre-warmed medium.

Organoids larger than 45 µM in size were counted at day 14 of culture to assess organoid forming

efficiencies, and were either fixed and stained at day 21 for analysis, or enzymatically dissociated into

single cells for further culture without sorting. Organoid lines were passaged at different days

depending on size, with culture days varying from 21-35 days. For passaging, Matrigel was disrupted

by incubation with Dispase (Sigma) at 37 ºC for 45 min, followed by single cell-dissociation through

addition of trypLE (Gibco) for 5 min at 37 ºC. The reaction was quenched with base medium, and cells

were centrifuged at 350 x g for 5 min. Cells were resuspended in fresh GFR-Matrigel at a ratio of 5 x

103 (48-well plates) or 10 x 103 (24-well transwell inserts) cells as before. HTII-280- cells were cultured

as with HTII-280+, although with a few minor differences. Bronchial organoids (hBOs) were passaged

every 21-28 days due to accelerated growth compared with alveolar organoids (hAOs), and were

cultured in previously reported medium conditions30 with the following concentration/factor edits;

100 ng/ml human FGF10, 10% R-SPONDIN-1, 10 µM SB431542 (instead of A83-01).

Virus infection to organoids and Vero cells

For hAO and hBO, cells were recovered from Matrigel with a Recovery solution (Corning). Organoids

were sheared with 1000 p pipette tips or incubated with Accutase (Stem cell technology) at 37 °C for

5 minutes. Organoids were resuspended with each organoid’s media and were infected with virus

multiplicity of infection (MOI) of 1 for 2 hrs at 37 °C 5% CO2. After virus infection, organoids were

washed twice with Advanced DMEM/F12 with 1 U/ml Penicillin/Streptomycin, 10 mM Hepes, and 1%

Glutamax (v/v) (hereafter referred to as ADF+++) and embedded with 50 µl of GFR-Matrigel (Corning)

in 24-well plate (TPP). At least each well contained 10,000 cells. Media and cells resuspended in

ADF+++ with Matrigel were taken at indicated time points. For viral particle release inside cells, cells

were lysed by freezing at -80 °C and thawing. Live virus titers were determined by plaque assay and

viral RNA titer was calculated using qPCR. For Vero cell infection, a virus with MOI 1 was poured on

Vero cells directly, and cells with viruses were incubated for 1 hr at 37 °C 5% CO2. After infection,

infection media was removed and cells were incubated in normal media in 37 °C 5% CO2 condition. All



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work was performed in a Class II Biosafety Cabinet under BSL-3 conditions at Korea Center for

Disease Control (KCDC).

Immunofluorescence staining of paraffin-embedded organoids

Organoids were fixed and embedded in a paraffin block43. Pre-cut 7 µM paraffin sections were de-

waxed and rehydrated (sequential immersion in xylene, 100% EtOH, 90% EtOH, 75% EtOH, distilled

water) and either stained with hematoxylin and eosin (H&E) or immunostained. For antigen retrieval,

slides were submerged into pre-heated citrate antigen retrieval buffer (10 mM sodium citrate, pH 6.0)

and allowed to boil for 15 min. Slides were cooled in a buffer for 20 min, washed in running water for 3

min, and permeabilised with 0.3% Triton-X in PBS for 15 min. Following permeabilisation, organoid

sections were blocked for 1 hr in 5% normal donkey serum in PBS at RT, and incubated with primary

antibody mixes overnight at 4 °C at the following dilutions; rabbit pro-SFTPC (1:500, Millipore,

Ab3786), mouse anti-HTII-280 (1:500, Terrace Biotech, TB-27AHT2-280), rat anti-SCGB1A1 (1:200,

R&D systems, MAB4218), rabbit anti-KRT5 (1:500, Biolegend, 905501), mouse anti-ABCA3 (1:300,

Seven Hills Bioreagents, WRAB-ABCA3), and mouse anti-TP63 (1:500, Abcam, ab735), rabbit anti-

HOPX (1:200, Santa Cruz Biotechnologies, sc-30216), and sheep anti-PDPN (1:200, R&D, AF3670).

Antibodies were removed with three PBS washes, and samples were incubated with Alexa Fluor-

couple secondary antibodies (1:1000, Jackson Laboratory) for 1 hr at RT. Following PBS washes,

nuclei were stained with DAPI for 5 min, slides mounted with Rapiclear (Sunjin lab), and sealed with

clear nail polish. For Lysotracker™ staining of lysosomes, live organoids in 48-well plates were

incubated in situ with 50 ng/µL of Lysotracker™ (Invitrogen, L12492), diluted in pre-warmed

expansion medium, for 1 hr at 37 °C. Lysotracker was removed, and organoid/matrigel suspension

was carefully washed for 5 min in PBS, followed by addition of fresh, pre-warmed expansion medium.

Cells were protected from light and imaged immediately using an EVOS cell imaging system.

Immunofluorescence staining of infected organoid with cryosection

Organoids were fixed in 4% paraformaldehyde (PFA) for 3 hrs at ice, and then dehydrated in PBS

with 30% sucrose (v/v) (Sigma). Organoids were embedded with optimal cutting temperature (OCT)

compound (Leica) and cut with 10 µM. Organoid section was blocked with 5% normal donkey serum

in PBS 1% triton-X (Sigma). Sections were incubated with primary antibodies overnight at 4 °C,



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washed three times with PBS. Organoids were incubated host matched Alexa Fluor-couple secondary

antibodies (Jackson Laboratory) with PBS for 1.5 hr at RT. Following DAPI incubation, slides were

mounted. Antibodies were E-cadherin (R&D, AF748, 1:300), NP (Sino, 40143-MM05, 1:200), Spike

S1 protein (Sino, 10030005, 1:200), ACE2 (abcam, ab15348, 1:400), TMPRSS2 (abcam,

EPR3861,1:400)

Transmission electron microscopy

Human alveolar organoids were fixed with 2.5 % glutaraldehyde in 0.1 M PBS overnight at 4 °C 44.

Organoids were washed with PBS and post-fixed with 2% osmium tetroxide for 1.5 hr. The fixed

sample was dehydrated in graded ethanol, substituted with propylene oxide, and finally embedded in

EMbed-812 resin (EMS). Polymerization was performed at 60 °C for 24 hrs. Ultrathin (100 nm)

sections were prepared using an ultramicrotome (Leica, EM UC7). Images were captured with a

transmission electron microscope (FEI Tecnai G2 spirit TWIN, eagle 4K CCD camera) at 120kV

acceleration voltage. All work was carried out in the EM & Histology Core Facility, at the BioMedical

Research Center, KAIST.

RNA purification

For viral RNA extraction, cells were resuspended with Matrigel with media and frozen at -80 °C and

then thawed. Thawed solution was processed through the QIAamp Viral RNA Mini Kit. For mRNA

extraction, cells were recovered from Matrigel by a Recovery solution (Corning) and then centrifuged

at 300 g for 5 min at 4 °C. Cell pellets were lysed, and RNA was extracted through RNeasy Plus Mini

Kit. For viral RNA extraction from media, 140 µl of media was processed through QIAamp Viral RNA

Mini Kit.

Assessment of lung lineage transcripts by qRT-PCR

Freshly sorted HTII-280+ and HTII-280- cells were lysed with TRIzol, and RNA was extracted. RNA

was reverse transcribed using SuperScript IV (Thermo Fisher Scientific), and were assessed using

the following Taqman probes; SFTPC (Hs00951326_g1), TP63 (Hs01114115_m1), SCGB1A1

(Hs00171092_m1).



https://www.thermofisher.com/taqman-gene-expression/product/Hs00951326_g1?CID=&ICID=&subtype=

https://www.thermofisher.com/taqman-gene-expression/product/Hs01114115_m1?CID=&ICID=&subtype=

https://www.thermofisher.com/taqman-gene-expression/product/Hs00171092_m1?CID=&ICID=&subtype=

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Viral RNA copy number calculation with qRT-PCR

Viral RNA samples were reverse-transcribed using SuperScript IV (Thermo Fisher Scientific). Viral N3

gene was targeted for qRT-PCR. Nucleotide sequences of the probes as below (CDC).

2019-nCoV_N3-F: 5’ GGGAGCCTTGAATACACCAAAA 3’

2019-nCoV_N3-R: 5’ TGTAGCACGATTGCAGCATTG 3’

Samples were prepared triplicate from solution. Viral RNA copy number was calculated by

comparing the standard curve of virus N3 gene. For generating the standard curve, the positive viral

RNA template was amplified with CDC designed N3 gene primers and cloned into pGEM-T Easy

vector (Promega, USA). The resultant plasmid DNA was linearized with PstI restriction enzyme and

purified with a QIAquick PCR Purification Kit (QIAGEN, Germany). Purified template was in vitro

transcribed by RiboMAX™ Large Scale RNA Production System with T7 RNA polymerase (Promega,

USA). RNA transcript was further purified with the NuceloSpin RNA Mini kit (MACHEREY-NAGEL,

Germany) and quantified with spectrophotometer at 260 nm. In vitro transcribed RNA was serially

diluted and reverse transcribed for the quantitative real time PCR45.

Live virus titer calculation with plaque assay

Matrigel was sheared with the organoid media and frozen at -80 °C once. Thaw the solution and dilute

by scale of 10. Each well containing Vero cells in 12 wells were infected with the diluted solution

respectively at 37 °C, 5% CO2 for 1 hr. After infection, remove infection media and wash the Vero

cells with PBS two times, mixed agar and Modified Eagle’s Medium (Thermofisher) were poured on

each well. When agar mixture was hardened, fix each well with 4% PFA for 3 days, and stain with

crystal violet (Sigma). When there are individual spots, the original solution’s viral titer was calculated.

Bulk RNA sequencing and data processing

Extracted cellular RNA was processed through Truseq Stranded Total RNA Gold kit, and cDNA library

was sequenced 2 x 100 bp using Hiseq 2500. Fastq file was aligned to GRCh38 with virus sequence

(NC 045512.2 from NCBI) using STAR46 and normalized RNA expression was calculated using

RSEM47. Differentially expressed genes are found from DEseq248. We obtained enriched gene sets



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using in-house scripts. For mutation calling, we used Strelka249, Varscan250, and Samtools51, and

then manually checked the position through IGV52.

Single-cell RNA sequencing and data processing

Fastq file was aligned and each UMI count was calculated using Cell Ranger software provided by the

manufacturer (10X Genomics). Cells with mitochondria RNA percent < 25%, total RNA number > 200

subsets were used for downstream analysis.

Starting from the 10X gene counts, we have normalized data as follows. The 10X data includes

SARS-CoV-2 genome as an extra gene besides 19,941 human genes. For uninfected cells, such

SARS-CoV-2 gene would have zero read count, while for infected cells, the gene could account for a

large portion of total reads. One of the goals of normalisation is to remove technical difference such

as sequencing coverage before comparing gene expression levels. As our interest is to compare

expression levels between uninfected and infected cells on human genes, we have applied a

normalisation method (‘scater R package’s ‘logNormCounts’ function) to human genes as a whole set.

The method calculated a scaling factor for each cell based on total human gene count, then scaled all

genes before taking log-transformation. Using the same scaling factor learned during human gene

normalisation, SARS-CoV-2 count was also normalized. For clustering, we combined single-cell data

from infected and uninfected hAOs. Unsupervised clustering was performed using a shared nearest

neighbor (SNN) based clustering algorithm in Seurat53. Contaminated cells (< 3%) were discarded. In

house R scripts were used for more downstream analyses.

Statistical inference on the effective number of viral entry

To assess whether alveolar cells tend to be infected by a single viral particle or multiple particles, we

employed a likelihood approach. As a proof-of-concept, we assumed only two scenarios exist, one

supporting a single viral entry and the other supporting double viral entry, then aimed to estimate the

proportion of cells with a single viral particle (𝑤). Out data consists of the observed reference (𝑛cell𝑟 )

and variant (𝑛𝑐𝑒𝑙𝑙𝑣 ) read counts for each of the 547 reporting at least one read at the mutation site of

NC_045512.2:23,707. Assuming a sequencing error rate (ε) of 0.1%, which will cover any Illumina

sequencing errors or misalignment, the likelihood of data given the weight supporting a single virus

scenario (𝑤) was computed as follows.



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𝐿(𝑤) = ∏ 𝐿𝑐𝑒𝑙𝑙(𝑤)

547

𝑐𝑒𝑙𝑙=1

𝐿𝑐𝑒𝑙𝑙(𝑤) = 𝑤 × 𝑃 (𝐷𝑎𝑡𝑎 𝑐𝑒𝑙𝑙| single entry) + (1 − 𝑤) × 𝑃 (𝐷𝑎𝑡𝑎 𝑐𝑒𝑙𝑙| double entry)

𝑃 (𝐷𝑎𝑡𝑎 𝑐𝑒𝑙𝑙| single entry) = 𝑃 (𝐺 = 𝑅 ) × 𝑃 (𝑛cell𝑟 , 𝑛𝑐𝑒𝑙𝑙

𝑣 | 𝑣𝑎𝑓𝑐𝑒𝑙𝑙 = ε) +

𝑃 (𝐺 = 𝑉 ) × 𝑃 (𝑛cell𝑟 , 𝑛𝑐𝑒𝑙𝑙

𝑣 | 𝑣𝑎𝑓𝑐𝑒𝑙𝑙 = 1 − ε)

𝑃 (𝐷𝑎𝑡𝑎 𝑐𝑒𝑙𝑙| double entry) = 𝑃 (𝐺 = 𝑅𝑅 ) × 𝑃 (𝑛cell𝑟 , 𝑛𝑐𝑒𝑙𝑙

𝑣 | 𝑣𝑎𝑓𝑐𝑒𝑙𝑙 = ε2 ) +

𝑃 (𝐺 = 𝑅𝑉 ) × 𝑃 (𝑛cell𝑟 , 𝑛𝑐𝑒𝑙𝑙

𝑣 | 𝑣𝑎𝑓𝑐𝑒𝑙𝑙 = 0.5 ) +

𝑃 (𝐺 = 𝑉𝑉 ) × 𝑃 (𝑛cell𝑟 , 𝑛𝑐𝑒𝑙𝑙

𝑣 | 𝑣𝑎𝑓𝑐𝑒𝑙𝑙 = (1 − 𝜀)2 )

G stands for the viral genotype within each cell, and R and V denote for the wildtype and mutant

alleles in the genotype, respectively. 𝑣𝑎𝑓𝑐𝑒𝑙𝑙 refers to the true mutant allele frequency within an

infected cell given the viral genotype after accounting for the sequencing error rate.

For both single- or double-entry scenarios, the prior for each viral genotype was computed using the

mutant allele frequency observed from the whole cell population (𝑣𝑎𝑓𝑝𝑜𝑝.; 4.3%=42 variant reads out

of 986 reads). For example, the prior of having the genotype with two mutant alleles in a double-entry

scenario is computed as follows:

𝑃 (𝐺 = 𝑉𝑉 ) = 𝑣𝑎𝑓𝑐𝑒𝑙𝑙2

Within a cell, the probability of observing the number of reference and variant reads given the mutant

allele frequency, 𝑃 (𝑛cell𝑟 , 𝑛𝑐𝑒𝑙𝑙

𝑣 | 𝑣𝑎𝑓𝑐𝑒𝑙𝑙), was computed using binomial distribution.

Supplementary discussion

Basal-like cells and hAT2 enrichment in single cell RNA sequencing

We observed a distinct transcriptional feature of cells in Cluster 2 which express lower levels of

canonical hAT2 marker genes, including SFTPC but detectable levels of airway marker genes, including

SOX2, TP63, KRT5, and KRT17 (Extended Data Fig. 6b). These expression patterns were not affected



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by virus infection. hAT2 cells expressing airway markers such as SOX2 were seen in chronic lung

diseases such as lung cancer and idiopathic pulmonary fibrosis (IPF)54,55, representing pathologic

phenotypes of alveolar bronchiolization. A recent study also suggested the potential transition of hAT2

cells to KRT5+ basal-like cells in the context of IPF27. Given the fact that the hAOs used for our single-

cell RNA sequencing study were derived from hAT2 cells isolated from adjacent normal counterparts

of lung cancer and/or IPF, it is likely that this transcriptional feature reflects the cellular status of original

tissues rather than virus-associated phenotype. This finding suggests that our hAO models maintain

the pathophysiologic features of original tissues although we used apparently normal background

regions for our hAO establishments. Further long-term tracing of changes in cellular identities and states

in response to virus infection in hAO cells will be of significant interest to understand the progression of

pathologic features and reparative mechanisms for developing therapeutic interventions.

Furthermore, from our scRNAseq analysis, most captured cells were hAT2 cells. It is likely that this

might result from the enrichment of hAT2 cells in our hAOs (P2) and the nature of fragile hAT1 cells

during the procedure of single-cell preparation for scRNAseq.



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Supplementary Tables

Supplementary Table 1. RNA expression levels (TPM) of all genes in seven human alveolar

organoid samples.

Supplementary Table 2. RNA expression levels (TPM) of all genes in seven human bronchial

(airway) organoid samples.

Supplementary Table 3. Twenty single base substitutions of SARS-CoV-2 which were detected in

infected organoids.

Acknowledgements

We thank Jinwook Choi (Wellcome-MRC Cambridge Stem Cell Institute), Yong Man Han, Eui-Cheol

Shin, Heung Kyu Lee, Su-Hyung Park, Jeong Seok Lee, Ryul Kim, Myungsuk Choi (KAIST), Jung-Ki

Yoon (Seoul National University Hospital), Yoon Ho Kim (Seoul National University Cancer Research

Institute), and Jong-Yeon Shin (Macrogen Inc.) for valuable discussion, productive comments, and

technical help; Irina Pshenichnaya (Histology), Peter Humphreys (Imaging), Simon McCallum (Flow

cytometry, Cambridge NIHR BRC Cell Phenotyping Hub), and Cambridge Stem Cell Institute core

facilities for technical assistance. This work was supported by Suh Kyungbae Foundation (SUHF-

18010082 to Y.S.J.); National Research Foundation of Korea (for Brain Pool Program NRF-

2019H1D3A2A02061168 to Y.S.J and S.Y.K; Leading Researcher Program NRF-

2020R1A3B2078973 to Y.S.J) and Institute for Basic Science (IBS-R025-D1, G.Y.K) funded by

Ministry of Science and ICT of Korea. J.-H.L was supported by Wellcome and the Royal Society

(107633/Z/15/Z) and European Research Council Starting Grant (679411). K.V.E was supported by

the Biotechnology and Biological Sciences Research Council Industrial CASE (BBSRC iCASE)

studentships (BB/R505328/1).

Author contributions

Conceptualization, Y.S.J. and J.-H.L.; hAO development, K.V.E., J.-H.L.; hAO establishments and

characterisation, K.V.E., J.Y., T.K., J.-H.L.; Specimen preparation; K.J.N, K.M., K.S., Y.T.K., K.M.,



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K.S.P.; Infection, J.K., J.Y., T.K., Y.-I.J., B.-S.C; Confocal microscope, K.V.E., S.P.H., G.Y.K.;

Electron microscope, Y.H., T.K., H.M.K.; Transcriptome analysis, J.Y., T.K., K.Y., S.Y.K., Y.S.J.;

Statistics, S.Y.K.; Writing, J.Y., T.K., K.V.E., T.B., S.-H.K., G.Y.K., Y.S.J., J.-H.L.; Manuscript

finalization, all authors; Supervision Y.S.J. and J.-H.L.

Declaration of interests

The authors declare no competing interests.

Materials availability

All unique organoids generated in this study are available from Young Seok Ju or Joo-Hyeon Lee with

a completed Materials Transfer Agreement.

Data availability

Bulk RNA and single cell RNA sequencing datasets will be uploaded on the European Genome-

Phenome Archive (EGA). Accession ID is not assigned yet.



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Figures and legends

Figure 1. Establishment of long-term and three-dimensional human alveolar type 2 cell culture

and SARS-CoV-2 infection in the three-dimensional model system



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a, Schematic diagram outlining the method for dissociation and processing of human adult lung

parenchymal tissues for in vitro three-dimensional cultures (top). Schematic illustration of SARS-CoV-

2 infection experiments in this study (bottom). b, Representative bright-field images of hAOs derived

from HTII-280+ hAT2 cells at day 28 in culture. Insets (top left) show high-power view of cystic-like

(top right) and budding-like (bottom left) alveolar organoids. Scale bar, 2000 µm. Of note, the majority

of hAOs exhibited uptake of Lysotracker (red), indicative of mature AT2 cells (bottom right). Scale bar,

1000 µm. c, Immunofluorescent staining of hAOs expressing AT2 markers. Left and right; HTII-280

(for hAT2, green), pro-SFTPC (for hAT2, red). Middle; ABCA3 (for hAT2, white). DAPI (blue). Scale

bar, 50 µm. d, Representative bright-field images of hBOs derived from HTII-280- non-hAT2 cells at

day 14 in culture. Insets (left) show high-power view (right). Scale bar, 2000 µm. e,

Immunofluorescent staining of hBOs expressing airway lineage markers. Left; SCGB1A1 (for

secretory, green), pro-SFTPC (for hAT2, red). Middle; KRT5 (basal, white), TP63 (basal, green), HTII-

280 (for hAT2, red). Right; ABCA3 (for hAT2, green), HOPX (for hAT1, white). DAPI (blue). Scale bar,

50 µm. f, Immunofluorescent staining of ACE2 (green) and HTII-280 (red) in hAOs. HTII-280 is

stained in the apical membrane of hAT2 cells. Scale bar, 100 µm. g, Immunofluorescent staining of

TMPRSS2 (green) in hAOs. Scale bar, 100 µm. h, Representative images for plaque assay at 3 dpi.

Dilution factors are shown in numbers on the right upper corner. Scale bar, 1 cm. i, Plaque assay

showing that SARS-CoV-2 actively replicates in hAOs at 1 dpi. SARS-CoV-2 can infect hBOs, but the

viral amplification is much lower than in hAOs. Given the different culture and viral infection

techniques between 2D Vero cells and 3D organoids, direct comparison is not applicable between

Vero cells and hAOs. Error bars represent SEM. n=2. j, Quantitative PCR (qPCR) analysis for

measuring the viral RNA levels in lysed hAOs. Error bars represent SEM. n=3. k, qPCR analysis for

measuring the viral RNA levels in hAO media. Error bars represent SEM. n=3.



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Figure 2. Confocal and transmission electron microscopic images of SARS-CoV-2 infected

hAOs

a, SARS-CoV-2 infected hAOs at 1 dpi. Viral nucleocapsid protein (NP) and double-stranded RNA

(dsRNA) are costained with pro-SFTPC. At 1 dpi, SARS-CoV-2 highly infects hAOs which show



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punctuated pattern of pro-SFTPC (red). Scale bar, 50 µm. b, The viruses are identified by dsRNA (top

right) and NP (bottom right). Infected hAOs express ACE2 (red). Viral dsRNA (green) appears

punctuated. More than 80% of hAT2s are infected at 1 dpi. Scale bar, 50 µm. c, Transmission

electron microscopic image of an entire hAO structure. Small alveolar spaces are shown between

hAT2s (red asterisk). Aggregated viral particles are sometimes observed in electron-dense areas of

the hAOs (white arrowhead). Scale bar (c-l), 1 µm. d, Lamellar bodies (skyblue arrow), consisting of

pulmonary surfactants, are frequently observed in hAOs. e, Hundreds of SARS-CoV-2 particles in the

cytosol of hAT2 cells (red arrow). f, Multiple viral particles in vesicular structures. g, SARS-CoV-2 viral

particles secreted into an alveolar space of hAOs. h, Another low magnification image of a hAO.

Some cells contain large pathologic vacuoles. i, Large vacuoles usually do not include virus particles,

but small vesicles contain many virus particles. j, Viral containing vesicles, aggregated in the vicinity

of zippered endoplasmic reticulum (ER; yellow arrow). k, Double membrane vesicles (red arrowhead)

located near zippered ER. l, Secreted viral particles in the apical lumen of hAOs. Microvilli (black

arrow) and lamellar body (skyblue arrow) are shown at the apical side of hAT2s.



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Figure 3. RNA-seq analyses of infected hAOs and hBOs

a, Heatmap of the most variable 100 genes among three groups of hAOs at 0, 1 and 3 dpi. Genes

related to type I interferon signal pathway are highly elevated at 3 dpi, while SARS-CoV-2 expression

reach plateau at 1 dpi. b, A volcano plot showing differentially expressed genes between hAOs at 0

dpi and 3 dpi. Most highly upregulated genes are in type I interferon signaling pathway. c,

Transcriptional changes of interferon genes in the infected hAOs. The rise of IFNB1, IFNL1, IFNL2,



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and IFNL3 are observed in hAOs. d, Proportion of viral RNA reads in the hAO and hBO

transcriptomes. Compared to hAOs, the portion of viral reads is much lower in hBOs. e, Positive-

sense viral RNAs are dominant (99.98%) over negative-sense viral RNAs. f, An example of missense

mutation (NC_045512.2: 3,177C>U) detected from hBO transcriptome at 3 dpi. The variant allele

fraction (VAF) of the substitution is 0.731 in an infected hBO sample, much higher than that in other

infected cells.



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Figure 4. Single-cell transcriptome analysis of uninfected and infected hAOs.

a, Unsupervised clustering of uninfected and infected human alveolar cells (3 dpi) in the UMAP plot.

b, Experimental conditions of the dataset projected on the UMAP plot. c, Numbers of viral transcripts

in each single-cell. Most cells harbor ≥2 viral reads. d, Normalized levels of SARS-CoV-2 viral read

counts in each cluster. e, Expression of genes of interest in single-cells of each cluster. f, Distribution

of single base substitution (variant allele fraction (VAF) = 4.3% in original viral particles) in each single

cell. g, Maximum likelihood estimation for the proportion of cells infected with a single virus. This



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estimation indicates that infection by a single virus is more common in hAT2s rather than by multiple

viruses.



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Extended Data Figure 1. Establishment and characterisation of three-dimensional culture of

human alveolar type 2 cells

a, Representative flow cytometry analysis for isolation of hAT2 cells (CD31–CD45–EPCAM+HTII-280+)

and non-hAT2 cells (nAT2, CD31–CD45–EPCAM+HTII-280–) from human distal lung parenchymal



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tissues. b, Quantitative PCR (qPCR) analysis of isolated primary human epithelial lung cells in (a) for

lung lineage marker genes revealed that HTII-280+ cells expressed much higher level of the AT2 cell

marker SFTPC, while HTII-280- cells expressed virtually no SFTPC and instead expressed higher

levels of the airway markers TP63 and SCGB1A1. Data is the mean ± SEM of two technical

replicates, and is expressed as arbitrary mRNA expression. c, Representative bright-field images of

organoids derived from hAT2s in complete medium (left) and withdrawal of CHIR99021 (right). Scale

bar, 2000 µm. d, Top; Organoid cultures for alveolar or bronchial cells from different donors were

passaged at various time points depending on growth. Each point represents a single passage.

Bottom; Statistical quantification of organoid forming efficiency at day 14-28 up to 9 passages

(alveolar) and 5 passages (bronchial). Each individual dot represents one technical replicate, and

data are presented as mean ± SEM for each individual donor sample. N=3 for hAOs; N=2 for hBOs. e,

Organoids continue to express pro-SFTPC (red) throughout culture (passage 5, left; passage 8, right),

even following 9 months in culture, but do not exhibit expression of the AT1 marker HOPX (white)

during later passages. DAPI (blue). Scale bar, 50 µM. f, Primary hAOs derived from hAT2 cells

demonstrate expression of the AT1 markers HOPX (green, left) and PDPN (green, right) and the AT2

markers HTII-280 (red, left) and pro-SFTPC (red, right panel). DAPI (blue). Scale bar, 50 µm. g,

Immunofluorescent staining of ACE2 and TMPRSS2 (green) is consistent in hAOs derived from

different donors. Scale bar, 50 µm.



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Extended Data Figure 2. Repeated experiments of SARS-CoV-2 infectivity in three-dimensional

culture of human alveolar type 2 cells.

a, qPCR analysis for measuring the viral RNA levels in hBOs, hAOs, and vero cells (Replicate #2).

SARS-CoV-2 infectivity is much higher (<100 times) in hAOs rather than hBOs. Error bars represent

SEM. n=2. b, qPCR analysis for measuring the viral RNA levels in hBOs and hAOs. (Replicate #3).

Error bars represent SEM. n=3



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Extended Data Figure 3. Immunofluorescent images of human alveolar type 2 organoids at 3

days after SARS-CoV-2 infection.

a, In a portion of the hAO at 3 dpi, SARS-CoV-2 infection is identified by viral nucleoprotein (NP) and

spike protein. Scale bar, 50 µm. b, SARS-CoV-2 infected hAOs still express the AT2 cell marker pro-

SFTPC. Scale bar, 50 µm.



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Extended Data Figure 4. Electron microscopy analysis of human alveolar type 2 cells infected

by SARS-CoV-2.

a, A low magnification image of an infected hAO. b, Viral particles (red arrow) in a large vacuole. The

density of viral particles is lower than that of small vesicle structures. c, Another low magnification

image of an infected hAO. Alveolar space is annotated by red asterisk. d, Viral particles are

encapsulated in the exocytic vesicles (red arrow). e, A low magnification of hAT2 cells. f, Viral

particles are attached to the outgoing vesicle (red arrow). Viruses are secreted in different ways.

Scale bar, 1 µm.



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Extended Data Figure 5. Transcriptome changes of the infected human lung organoids.

a, Correlation of TPM level of each gene in hAOs at 0 and 3 dpi. Each gene is depicted as a blue dot.

b, Heatmap of the most variable 100 genes derived from hAOs (Figure 5A, Table S1) in SARS-CoV-2

infected hBOs. Normalisation of gene expression was calculated using all transcriptome data from 14

samples of hAOs and hBOs. c, Volcano plot for differentially expressed genes in hBOs at 0 and 3 dpi.

No significant changes were shown in ISGs.



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Extended Data Figure 6. Single-cell transcriptome profiling of infected hAOs.

a, Box plots showing normalised expression of interested genes in each cluster. Center line, median;

box limits, upper and lower quartiles; whiskers, 1.5x interquartile range. b, In Cluster 2, expression

level of SFTPC, one of hAT2 cell marker genes is reduced. By contrast, some airway marker genes

such as SOX2, TP63, and KRT17 were expressed.



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References

1 Fung, T. S. & Liu, D. X. Human Coronavirus: Host-Pathogen Interaction. Annu Rev Microbiol

73, 529-557, doi:10.1146/annurev-micro-020518-115759 (2019).

2 Wu, F. et al. A new coronavirus associated with human respiratory disease in China. Nature

579, 265-269, doi:10.1038/s41586-020-2008-3 (2020).

3 WHO. World Health Organization Coranavirus disease (COVID-19) Situation Report -140.

(2020).

4 Muus, C. et al. Integrated analyses of single-cell atlases reveal age, gender, and smoking

status associations with cell type-specific expression of mediators of SARS-CoV-2 viral entry

and highlights inflammatory programs in putative target cells. bioRxiv (2020).

5 Hoffmann, M. et al. SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked

by a Clinically Proven Protease Inhibitor. Cell 181, 271-280 e278,

doi:10.1016/j.cell.2020.02.052 (2020).

6 Kim, D. et al. The Architecture of SARS-CoV-2 Transcriptome. Cell 181, 914-921 e910,

doi:10.1016/j.cell.2020.04.011 (2020).

7 Walls, A. C. et al. Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein.

Cell 181, 281-292 e286, doi:10.1016/j.cell.2020.02.058 (2020).

8 Wang, Q. et al. Structural and Functional Basis of SARS-CoV-2 Entry by Using Human ACE2.

Cell 181, 894-904 e899, doi:10.1016/j.cell.2020.03.045 (2020).

9 Chu, H. et al. Comparative tropism, replication kinetics, and cell damage profiling of SARS-

CoV-2 and SARS-CoV with implications for clinical manifestations, transmissibility, and

laboratory studies of COVID-19: an observational study. The Lancet Microbe 1, e14-e23,

doi:10.1016/S2666-5247(20)30004-5 (2020).

10 Bao, L. et al. The pathogenicity of SARS-CoV-2 in hACE2 transgenic mice. Nature,

doi:10.1038/s41586-020-2312-y (2020).

11 Kim, Y. I. et al. Infection and Rapid Transmission of SARS-CoV-2 in Ferrets. Cell Host Microbe

27, 704-709 e702, doi:10.1016/j.chom.2020.03.023 (2020).

12 Sia, S. F. et al. Pathogenesis and transmission of SARS-CoV-2 in golden hamsters. Nature,

doi:10.1038/s41586-020-2342-5 (2020).

13 Wolfel, R. et al. Virological assessment of hospitalized patients with COVID-2019. Nature

581, 465-469, doi:10.1038/s41586-020-2196-x (2020).

14 Andersen, K. G., Rambaut, A., Lipkin, W. I., Holmes, E. C. & Garry, R. F. The proximal origin

of SARS-CoV-2. Nat Med 26, 450-452, doi:10.1038/s41591-020-0820-9 (2020).

15 Forster, P., Forster, L., Renfrew, C. & Forster, M. Phylogenetic network analysis of SARS-CoV-

2 genomes. Proc Natl Acad Sci U S A 117, 9241-9243, doi:10.1073/pnas.2004999117 (2020).

16 Shang, J. et al. Structural basis of receptor recognition by SARS-CoV-2. Nature 581, 221-224,

doi:10.1038/s41586-020-2179-y (2020).

17 Fatehullah, A., Tan, S. H. & Barker, N. Organoids as an in vitro model of human development

and disease. Nat Cell Biol 18, 246-254, doi:10.1038/ncb3312 (2016).



https://doi.org/10.1101/2020.07.10.194498


18 Heo, I. et al. Modelling Cryptosporidium infection in human small intestinal and lung

organoids. Nat Microbiol 3, 814-823, doi:10.1038/s41564-018-0177-8 (2018).

19 Bartfeld, S. et al. In vitro expansion of human gastric epithelial stem cells and their responses

to bacterial infection. Gastroenterology 148, 126-136 e126, doi:10.1053/j.gastro.2014.09.042

(2015).

20 Elbadawi, M. & Efferth, T. Organoids of human airways to study infectivity and cytopathy of

SARS-CoV-2. Lancet Respir Med, doi:10.1016/S2213-2600(20)30238-1 (2020).

21 Lamers, M. M. et al. SARS-CoV-2 productively infects human gut enterocytes. Science,

doi:10.1126/science.abc1669 (2020).

22 Monteil, V. et al. Inhibition of SARS-CoV-2 Infections in Engineered Human Tissues Using

Clinical-Grade Soluble Human ACE2. Cell 181, 905-913 e907, doi:10.1016/j.cell.2020.04.004

(2020).

23 Yang, L. et al. A Human Pluripotent Stem Cell-based Platform to Study SARS-CoV-2 Tropism

and Model Virus Infection in Human Cells and Organoids. Cell Stem Cell 27, 125-136 e127,

doi:10.1016/j.stem.2020.06.015 (2020).

24 Barkauskas, C. E. et al. Lung organoids: current uses and future promise. Development 144,

986-997, doi:10.1242/dev.140103 (2017).

25 Barkauskas, C. E. et al. Type 2 alveolar cells are stem cells in adult lung. J Clin Invest 123,

3025-3036, doi:10.1172/Jci68782 (2013).

26 Zacharias, W. J. et al. Regeneration of the lung alveolus by an evolutionarily conserved

epithelial progenitor. Nature 555, 251-255, doi:10.1038/nature25786 (2018).

27 Kathiriya, J. J. et al. Human alveolar Type 2 epithelium transdifferentiates into metaplastic

KRT5+ basal cells during alveolar repair. bioRxiv (2020).

28 Gonzalez, R. F., Allen, L., Gonzales, L., Ballard, P. L. & Dobbs, L. G. HTII-280, a biomarker

specific to the apical plasma membrane of human lung alveolar type II cells. J Histochem

Cytochem 58, 891-901, doi:10.1369/jhc.2010.956433 (2010).

29 Nikolic, M. Z. et al. Human embryonic lung epithelial tips are multipotent progenitors that

can be expanded in vitro as long-term self-renewing organoids. Elife 6,

doi:10.7554/eLife.26575 (2017).

30 Sachs, N. et al. Long-term expanding human airway organoids for disease modeling. EMBO

J 38, doi:10.15252/embj.2018100300 (2019).

31 Kim, J. M. et al. Identification of Coronavirus Isolated from a Patient in Korea with COVID-

19. Osong Public Health Res Perspect 11, 3-7, doi:10.24171/j.phrp.2020.11.1.02 (2020).

32 Suzuki, T. et al. Generation of human bronchial organoids for SARS-CoV-2 research. bioRxiv

(2020).

33 Knoops, K. et al. SARS-coronavirus replication is supported by a reticulovesicular network of

modified endoplasmic reticulum. PLoS Biol 6, e226, doi:10.1371/journal.pbio.0060226 (2008).

34 Arbour, N. et al. Acute and persistent infection of human neural cell lines by human

coronavirus OC43. J Virol 73, 3338-3350 (1999).



https://doi.org/10.1101/2020.07.10.194498


35 Maier, H. J. et al. Infectious bronchitis virus generates spherules from zippered endoplasmic

reticulum membranes. mBio 4, e00801-00813, doi:10.1128/mBio.00801-13 (2013).

36 Ogando, N. S. et al. SARS-coronavirus-2 replication in Vero E6 cells: replication kinetics, rapid

adaptation and cytopathology. bioRxiv (2020).

37 Iwasaki, A., Foxman, E. F. & Molony, R. D. Early local immune defences in the respiratory

tract. Nat Rev Immunol 17, 7-20, doi:10.1038/nri.2016.117 (2017).

38 Schneider, W. M., Chevillotte, M. D. & Rice, C. M. Interferon-stimulated genes: a complex

web of host defenses. Annu Rev Immunol 32, 513-545, doi:10.1146/annurev-immunol-

032713-120231 (2014).

39 Forero, A. et al. Differential Activation of the Transcription Factor IRF1 Underlies the Distinct

Immune Responses Elicited by Type I and Type III Interferons. Immunity 51, 451-464 e456,

doi:10.1016/j.immuni.2019.07.007 (2019).

40 Park, A. & Iwasaki, A. Type I and Type III Interferons - Induction, Signaling, Evasion, and

Application to Combat COVID-19. Cell Host Microbe, doi:10.1016/j.chom.2020.05.008 (2020).

41 Ziegler, C. G. K. et al. SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human

Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues. Cell 181, 1016-

1035 e1019, doi:10.1016/j.cell.2020.04.035 (2020).

42 Blanco-Melo, D. et al. Imbalanced Host Response to SARS-CoV-2 Drives Development of

COVID-19. Cell 181, 1036-1045 e1039, doi:10.1016/j.cell.2020.04.026 (2020).

43 Lee, J. H. et al. Lung stem cell differentiation in mice directed by endothelial cells via a

BMP4-NFATc1-thrombospondin-1 axis. Cell 156, 440-455, doi:10.1016/j.cell.2013.12.039

(2014).

44 Fujii, M. et al. Human Intestinal Organoids Maintain Self-Renewal Capacity and Cellular

Diversity in Niche-Inspired Culture Condition. Cell Stem Cell 23, 787-793 e786,

doi:10.1016/j.stem.2018.11.016 (2018).

45 Kim, J. H. et al. Clinical diagnosis of early dengue infection by novel one-step multiplex real-

time RT-PCR targeting NS1 gene. J Clin Virol 65, 11-19, doi:10.1016/j.jcv.2015.01.018 (2015).

46 Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15-21,

doi:10.1093/bioinformatics/bts635 (2013).

47 Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or

without a reference genome. BMC Bioinformatics 12, 323, doi:10.1186/1471-2105-12-323

(2011).

48 Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for

RNA-seq data with DESeq2. Genome Biol 15, 550, doi:10.1186/s13059-014-0550-8 (2014).

49 Saunders, C. T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor-

normal sample pairs. Bioinformatics 28, 1811-1817, doi:10.1093/bioinformatics/bts271

(2012).

50 Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in

cancer by exome sequencing. Genome Res 22, 568-576, doi:10.1101/gr.129684.111 (2012).



https://doi.org/10.1101/2020.07.10.194498


51 Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078-

2079, doi:10.1093/bioinformatics/btp352 (2009).

52 Robinson, J. T. et al. Integrative genomics viewer. Nat Biotechnol 29, 24-26,

doi:10.1038/nbt.1754 (2011).

53 Stuart, T. et al. Comprehensive Integration of Single-Cell Data. Cell 177, 1888-1902 e1821,

doi:10.1016/j.cell.2019.05.031 (2019).

54 Jensen-Taubman, S. M., Steinberg, S. M. & Linnoila, R. I. Bronchiolization of the alveoli in

lung cancer: pathology, patterns of differentiation and oncogene expression. Int J Cancer

75, 489-496, doi:10.1002/(sici)1097-0215(19980209)75:4<489::aid-ijc1>3.0.co;2-p (1998).

55 Xu, Y. et al. Single-cell RNA sequencing identifies diverse roles of epithelial cells in idiopathic

pulmonary fibrosis. JCI Insight 1, e90558, doi:10.1172/jci.insight.90558 (2016).



https://doi.org/10.1101/2020.07.10.194498


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